Diabetic eye disease and namely retinopathy (DR), a complication of diabetes mellitus, is the leading cause of blindness in the US working age population (age 20-74 years) and thus has significant socio-economic consequences. There is abundant evidence that blindness and visual loss in these patients can be prevented through annual screening and early diagnosis. The number of patients with diabetes is increasing rapidly and is estimated to be nearly 26 million in the US and about 8 million of these individuals have DR. More than 50% of these are underserved who do not receive regular eye examination by eye care providers. The burden on the medical system to serve this segment of population can be significant. Fundus images of the human retina are an important diagnostic tool to detect and monitor the progression of many retinal and optic nerve diseases including age-related macular degeneration, diabetic retinopathy, and glaucoma. Photographic systems for fundus imaging are common in ophthalmological practices, and are increasingly becoming available in primary care clinics, including those serving poor, rural or remote populations. In this latter case, the images are often acquired by nonspecialists and then later transmitted over secure digital networks to physicians for further screening and diagnosis. This store and forward system is referred to as teleophthalmology. Teleophthalmology based diabetic eye disease screening programs based in primary care clinics have helped to increase the rates of annual eye screenings for vulnerable populations by over 50%. Due to the practical delays between image acquisition and specialist grading, a problem arises when the remote ophthalmologist or eye care provider determines the collected images are of insufficient quality for screening, but the patient has already left the clinic due to the store and forward nature of image analysis. Examples of insufficient quality include improper positioning of the image field, out-of-focus images, or insufficient illumination, which would result in a lack of image data for a given area of the retina. In remote rural areas or other underserved regions, this delay in quality assessment could introduce significant additional burden for the patient requiring a return trip to the clinic. This additional visit may be too high a barrier especially for the underserved. Without a proper screening, patients may continue with undiagnosed disease and miss out on early treatment. Thus it would be desirable if an assessment of fundus image quality could occur at the time of image capture, allowing the system operator to collect new images with sufficient quality for diagnosis. While training the operators is one approach to this assessment, there may be limitations in the ability of the personnel to be trained, or subtleties in the images that suggest an automated system may be most desirable. The overarching goal is to make the early detection and treatment of DR more accessible by improving efficiency in the acquisition of quality retinal images. Achieving consistent quality for retinal images is highly sought after by regulators evaluating the safety and efficacy of such novel methods of improving access to quality health care, such as the FDA.